KDD2019 workshop W14: Explainable AI/ML (XAI) for Accountability, Fairness, and Transparency


[まとめページへのリンク](https://qiita.com/kotaaaa/items/4f144d660d504cc091cf)

Explainable AI/ML (XAI) for Accountability, Fairness, and Transparency
Page: https://xai.kdd2019.a.intuit.com/

#### Keynote: Responsible Use of Explainable Artificial Intelligence 
Patrick Hall
https://github.com/jphall663/kdd_2019/

Intro
Understanding the trust 
The dark side 
Surrogates


Intro 
What is an explanaition in ML?
What do I mean by explainable ML?
Mostly post-hoc techniques users to enhance understanding od trained model mechanism and prediction 
Direct measures od global and local feature 

Responsible use of explainable ML can enable 

- Human learning from machine learning 
- Human appeal of automated decision 
- Regulatory compliance 
- White-hat hacking and security audits of ML model 
Even logistic regression is often “explained ",or post-processed for credit scoring, eg)Max points lost method and adverse action notices.

Why propose guidelines

- Misuse and abuseof explainable ML can enable 
- ExplainableML is already in-use
- Regulatory guidance is not agreed upon yet

Proposed guidelines for responsible use

- Use explainable ML to enhance understaning 
- Learn how explainable ML is used for nefarious purpose.
- Augment surrogate models with direct explanations
- Use highly transparent machanism for high stakes application 

Use explainable ML to enhance understaning 

Use explainable ML to enhance understaning 
corollary2.1: White-hat attackes can crack potentially harmful black-box
corollary2.2: Explanation is not a front line fairness tool
Surrogates models
BUT many explainable ML techniques have nothing to do with surrogate models 

Augment surrogate models with direct explanations
corollary3.1: augment LIME with direct explanations

Use highly transparent machanism for high stakes application 
corollary4.1: explanations and interpretable models are not mutually exclusive.
Interrude: an ode to the shaply values
corollary4.3: explanations and fairness techniques are not mutually exclusive 



#### Contributed Talk: Parametrised Data Sampling for Fairness Optimisation 
Carlos Vladimiro Gonzalez Zelaya, Paolo Missier, and Dennis Prangle

Slides: https://github.com/vladoxNCL/fairCorrect/blob/master/Presentation.pdf

outline
- Method for correcting classifier fairness
- Model and definition agonistic 
- Tune correction level to optimize fairness

Train set correlation 
Sampling strategies

- Original data
- Under
- SMOTE[chawia et al 2002]
- Preferential 
Equality form 
Ratio form 
Some fairness ratios
- Demographic parity 
- Equality of opportunity 
- Counterfactual (proxy)
Effect on test set (COMPAS, Undersampling)
- Effect is correlated with correction 
- But it occurs to a different effect 
- Intersection is not at d=0
Optional correction by fairness and sampling 

Accuracy vs fairness trade-off
Conjecture: A LR model is FTU <> CFR = 1

Conclusion 
- Fairness-agonistic optimization with a relatively small loss in accuracy 
- ideal correction level is definition dependent 
- Different sampling strategy produced similar result 


Contributed Talk: What Makes a Good Explanation? The Lemonade Stand Game As a Platform for Evaluating XAI 
Talia Tron, Daniel Ben David, Tzvika Barenholz, and Yehezkel S. Resheff

Title: What can selling lemonade teach us about XAI
What to explain:
Nothing > the data > the model > the prediction 
Model transparency 
Global explanation 
local explanation 

What makes a good explanation 
Explanation quality 
Users understanding 
> user behavior adoption trust, satisfaction 

The challenge 

- Behabioal measures 
- Dynamic 
- Real financial 
- Counrolled environment 
- Uncertain conditions 

Research questions
- Evaluate the influence of different explanations on financial alporism advice adoption 

Experiment: the situation of selling lemonede.

#### Keynote: Interpretability - now what? 
Been Kim
Sea of interpretability 

- Where are we going ?
- What do we have now ?
- What can we do better ?
- What should we be carefull  ?

We need to ensure to 
1, Our values are aligned 
2, Our knowledge is reflected for everyone.

Not out goal

- About making all models interpretable
- About understanding every single bit about the model 
- Against developing highly complex models
- Only about gaining user trust of fairness

What do we have now ?
Investing post-training interpretability 
Saliency Maps
Some confusing behavior of saliency Maps  
Sanity checlk1 
When prediction changes do explanations change ? NO
Network rained with true and random labels do explanations deliver different explanation

What can we learn from this?

- Confirmation bias 
- Others who independently reached the same conclusions 
- Some of these methods have been shown to be useful for humans why ? More studyes are needed .
- Resent work by Gupta Arora 19’ suggests a simple fix


Goal of TCAV: Testing with concept activation vectors
Defining concept activation vector (CAV)
TCAV core idea: derivative with CAV to get prediction sensitivity 
Guiding against spurious CAV
Quantitive validation : guarding against spurious CAV
Recap TCAV: testing with concept activation vectors.


Limitation of TCAV
Concept ohas to “exoressible” Using examples
User needs to know which concepts they want to test 
Explanation is Not-casual 


What should we be carefull ?
Proper evaluation 
Remember that humans are biasied and irrational
Design the right interaction (HCI)
Try to criticize (think about what wasn’t talked about in this talk but should have  )
keep checking